# fengbintu/Neural-Networks-on-Silicon

This is originally a collection of papers on neural network accelerators. Now it's more like my selection of research on deep learning and computer architecture.

Repository: https://github.com/fengbintu/Neural-Networks-on-Silicon
Canonical: https://ross.abutalabs.com/products/neural-networks-on-silicon
License Family: other
Topics: deep-learning, hardware
Last push: 2026-03-30T01:47:23+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 74, release rhythm 35, longevity 100
- inputs: {"age_days": 3873, "days_push": 157, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2118, forks 393 (observed 2026-08-28T04:06:16.382354+00:00)

## What it is
A curated collection of research papers on neural network accelerators and the intersection of deep learning and computer architecture. It organizes papers by year and venue (ISSCC, ISCA, MICRO, HPCA, ASPLOS, DAC, FPGA, HotChips, etc.) and includes the maintainer's own contributions.

## Use cases
- find papers on neural network accelerator design
- survey AI chip research by year and conference
- keep up with deep learning hardware research
- prepare a literature review on DNN accelerators
- learn about efficient hardware for deep learning inference and training
- track ISSCC and HotChips AI chip presentations

## When to choose
- you need a comprehensive, organized reading list of AI accelerator papers
- you are a researcher or student surveying deep learning hardware architecture
- you want to follow top-venue publications (ISCA, MICRO, ISSCC, HPCA) on AI chips

## When to avoid
- you need runnable code or an implementation of an accelerator
- you want a tutorial or textbook-style introduction rather than paper links
- you need licensed, redistributable content (the repo has no license)

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: deep-learning, hardware, awesome-lists, tutorials
- platform: cross-platform
- tags: paper-collection, ai-accelerators, computer-architecture, curated-list, research-papers

## Member repositories
- fengbintu/Neural-Networks-on-Silicon (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:16.382354+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:53:03.120672+00:00, confidence not recorded.
  - readme: https://github.com/fengbintu/Neural-Networks-on-Silicon (fetched 2026-08-28T04:06:16.382354+00:00, sha 39aeda82eea5)
- Data as of 2026-08-30T08:39:29.467469+00:00.